From 6bb559d33a150bde8a798fc27deac481a4f9aa5a Mon Sep 17 00:00:00 2001
From: runllm <support@runllm.com>
Date: Fri, 18 Apr 2025 15:18:24 +0000
Subject: [PATCH 1/3] Update metadata-ingestion.md

---
 docs/architecture/metadata-ingestion.md | 91 +++++++++++++++++++++++++
 1 file changed, 91 insertions(+)

diff --git a/docs/architecture/metadata-ingestion.md b/docs/architecture/metadata-ingestion.md
index abf8fc24d1385..821035918af08 100644
--- a/docs/architecture/metadata-ingestion.md
+++ b/docs/architecture/metadata-ingestion.md
@@ -25,6 +25,97 @@ DataHub ships with a Python based [metadata-ingestion system](../../metadata-ing
 
 As long as you can emit a [Metadata Change Proposal (MCP)] event to Kafka or make a REST call over HTTP, you can integrate any system with DataHub. For convenience, DataHub also provides simple [Python emitters] for you to integrate into your systems to emit metadata changes (MCP-s) at the point of origin.
 
+### Emitting Metadata for Containers and Datasets
+
+To emit metadata for both datasets and their containers using the DataHub Python SDK, follow these steps:
+
+1. **Install the DataHub Python SDK**:
+   ```bash
+   pip install acryl-datahub
+   ```
+
+2. **Initialize DataHubRestEmitter**: Set up the `DataHubRestEmitter` with your DataHub GMS endpoint.
+
+3. **Create and Emit Metadata for the Dataset**:
+   - Use `MetadataChangeProposalWrapper` with `DatasetPropertiesClass` to set dataset properties.
+
+4. **Create and Emit Metadata for the Container**:
+   - Use `MetadataChangeProposalWrapper` with `ContainerPropertiesClass` to set container properties.
+
+5. **Link Dataset to Container** (Optional):
+   - Use `ContainerClass` to link the dataset to its container and emit the relationship.
+
+Example code snippet:
+
+```python
+from datahub.emitter.rest_emitter import DatahubRestEmitter
+from datahub.metadata.schema_classes import (
+    MetadataChangeProposalWrapper,
+    DatasetPropertiesClass,
+    ContainerPropertiesClass,
+    ChangeTypeClass,
+    ContainerClass
+)
+
+# Initialize the DataHub REST emitter
+gms_endpoint = "http://localhost:8080"  # Replace with your DataHub GMS endpoint
+emitter = DatahubRestEmitter(gms_endpoint)
+
+# Define the dataset URN
+dataset_urn = "urn:li:dataset:(urn:li:dataPlatform:your_platform,your_dataset,PROD)"
+
+# Define the container URN
+container_urn = "urn:li:container:(urn:li:dataPlatform:your_platform,your_container,PROD)"
+
+# Create the DatasetProperties aspect
+dataset_properties = DatasetPropertiesClass(
+    description="This is a sample dataset",
+    customProperties={"key": "value"}
+)
+
+# Create the MetadataChangeProposal for the dataset
+dataset_mcp = MetadataChangeProposalWrapper(
+    entityType="dataset",
+    entityUrn=dataset_urn,
+    changeType=ChangeTypeClass.UPSERT,
+    aspect=dataset_properties
+)
+
+# Emit the MetadataChangeProposal for the dataset
+emitter.emit_mcp(dataset_mcp)
+
+# Create the ContainerProperties aspect
+container_properties = ContainerPropertiesClass(
+    name="Your Container Name",
+    description="Description of your container",
+    customProperties={"key": "value"}
+)
+
+# Create the MetadataChangeProposal for the container
+container_mcp = MetadataChangeProposalWrapper(
+    entityType="container",
+    entityUrn=container_urn,
+    changeType=ChangeTypeClass.UPSERT,
+    aspect=container_properties
+)
+
+# Emit the MetadataChangeProposal for the container
+emitter.emit_mcp(container_mcp)
+
+# Optionally, link the dataset to the container
+link_mcp = MetadataChangeProposalWrapper(
+    entityType="dataset",
+    entityUrn=dataset_urn,
+    changeType=ChangeTypeClass.UPSERT,
+    aspect=ContainerClass(container=container_urn)
+)
+
+# Emit the link between the dataset and the container
+emitter.emit_mcp(link_mcp)
+```
+
+This approach ensures that both the dataset and its container are represented in DataHub, and the dataset is linked to its container.
+
 ## Internal Components
 
 ### Applying Metadata Change Proposals to DataHub Metadata Service (mce-consumer-job)

From d8be5164f868c143bb2928451d7328ffb06c674d Mon Sep 17 00:00:00 2001
From: runllm <support@runllm.com>
Date: Fri, 18 Apr 2025 15:18:25 +0000
Subject: [PATCH 2/3] Update mxe.md

---
 docs/what/mxe.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/what/mxe.md b/docs/what/mxe.md
index 25294e04ea3d9..89131b1931069 100644
--- a/docs/what/mxe.md
+++ b/docs/what/mxe.md
@@ -23,6 +23,8 @@ MCPs may be emitted by clients of DataHub's low-level ingestion APIs (e.g. inges
 during the process of metadata ingestion. The DataHub Python API exposes an interface for 
 easily sending MCPs into DataHub. 
 
+To emit metadata for both datasets and containers, you can use the `MetadataChangeProposalWrapper` class in the DataHub Python SDK. This involves creating and emitting separate `MetadataChangeProposalWrapper` instances for each entity, ensuring that both the dataset and its container are properly represented in DataHub. You can also link a dataset to its container using the `ContainerClass` aspect.
+
 The default Kafka topic name for MCPs is `MetadataChangeProposal_v1`.
 
 ### Consumption

From e5cb3e23c4cb996baf06bcd81f1b5d16fa8d9388 Mon Sep 17 00:00:00 2001
From: runllm <support@runllm.com>
Date: Fri, 18 Apr 2025 15:18:26 +0000
Subject: [PATCH 3/3] Update datahub-apis.md

---
 docs/api/datahub-apis.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/api/datahub-apis.md b/docs/api/datahub-apis.md
index c46aacde3a0cb..e38c145eeb082 100644
--- a/docs/api/datahub-apis.md
+++ b/docs/api/datahub-apis.md
@@ -19,6 +19,7 @@ We offer an SDK for both Python and Java that provide full functionality when it
 - Define a lineage between data entities
 - Executing bulk operations - e.g. adding tags to multiple datasets
 - Creating custom metadata entities
+- Emitting metadata for datasets and containers using `MetadataChangeProposalWrapper`
 
 Learn more about the SDKs: